Briefly, given a set of query genes provided the engine, a relevance weight is determined for each dataset
based on how well correlated the query genes are in each dataset. Datasets where the query genes are largely
co-expressed recive a high weight, while datasets where the query genes do not agree are given a low weight.
Based on these per-dataset weights, weighted correlations are calculated for every other gene in the genome
to the query set. In this way genes that agree with the query set in datasets where the query is consistent
will achieve the best results, while genes that agree with some of the query set in datasets where the query is
not co-expressed will recieve a low result. Datasets and genes are sorted by their correlation scores and
returned for each query.

There are two edge cases not discussed in the main text that you may encounter:
First, if no datasets are found to contain a significant signal for a given query then we are unable to assign
per-dataset weights for the search. In this case a warning message is displayed, and all datasets are equally
weighted for that query.
Second, if no genes are related to the query set at a reliable confidence level, then a warning message is
displayed and the confidence level is weakened until results can be obtained.
Both of these cases typically only occur when the query genes are either largerly unrelated, or highly unique.
Neither of these cases occurs very often.